original_pop[2:nrow(original_pop),3:ncol(original_pop)] %>%
ggpairs(title="경기도 시군별 인구수 및 세대수")

covid_before_after = str_remove(original_data$기준년월, "-") %>%
as.numeric() >= 202004
data = original_data %>%
select(c("시군명", "기준년월", "월별카드발행수량(건)", "월별카드충전액(천원)", "월별카드사용액(천원)")) %>%
mutate(covid=if_else(covid_before_after, 'after', 'before'))
original_pop %>% colnames()
## [1] "시군구별(1)" "시군구별(2)" "합계(명)" "세대수(세대)"
## [5] "총내국인(명)" "남내국인(명)" "여내국인(명)" "등록외국인 (명)"
# 현재 총 인구 합계로 표준화
pop = original_pop %>%
select(c("시군구별(1)", "세대수(세대)")) %>%
rename(시군명=`시군구별(1)`, total=`세대수(세대)`)
skimr::skim(pop)
Data summary
| Name |
pop |
| Number of rows |
32 |
| Number of columns |
2 |
| _______________________ |
|
| Column type frequency: |
|
| character |
1 |
| numeric |
1 |
| ________________________ |
|
| Group variables |
None |
Variable type: character
Variable type: numeric
| total |
0 |
1 |
357328.2 |
987554.8 |
22060 |
79134 |
147892.5 |
287895.2 |
5717252 |
▇▁▁▁▁ |
div_data = data %>% mutate_at(vars(-시군명, -기준년월, -covid), funs(. / `월별카드발행수량(건)` ))
## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
# 표준화
stand_data = data %>% left_join(pop, by='시군명') %>% mutate_at(vars(-시군명, -기준년월, -covid), funs(. / total))
데이터 개관
Data summary
| Name |
data |
| Number of rows |
837 |
| Number of columns |
6 |
| _______________________ |
|
| Column type frequency: |
|
| character |
3 |
| numeric |
3 |
| ________________________ |
|
| Group variables |
None |
Variable type: character
| 시군명 |
0 |
1 |
3 |
4 |
0 |
31 |
0 |
| 기준년월 |
0 |
1 |
7 |
7 |
0 |
27 |
0 |
| covid |
0 |
1 |
5 |
6 |
0 |
2 |
0 |
Variable type: numeric
| 월별카드발행수량(건) |
159 |
0.81 |
7349.39 |
18945.03 |
1 |
1141.25 |
2956 |
6786.5 |
279776 |
▇▁▁▁▁ |
| 월별카드충전액(천원) |
141 |
0.83 |
4840481.82 |
6307753.76 |
6890 |
924241.25 |
2434881 |
6133970.2 |
56389896 |
▇▁▁▁▁ |
| 월별카드사용액(천원) |
141 |
0.83 |
4351264.15 |
5615195.21 |
0 |
750687.25 |
2141071 |
5648037.2 |
32642111 |
▇▁▁▁▁ |
Data summary
| Name |
stand_data |
| Number of rows |
837 |
| Number of columns |
7 |
| _______________________ |
|
| Column type frequency: |
|
| character |
3 |
| numeric |
4 |
| ________________________ |
|
| Group variables |
None |
Variable type: character
| 시군명 |
0 |
1 |
3 |
4 |
0 |
31 |
0 |
| 기준년월 |
0 |
1 |
7 |
7 |
0 |
27 |
0 |
| covid |
0 |
1 |
5 |
6 |
0 |
2 |
0 |
Variable type: numeric
| 월별카드발행수량(건) |
159 |
0.81 |
0.04 |
0.07 |
0.00 |
0.01 |
0.02 |
0.04 |
0.79 |
▇▁▁▁▁ |
| 월별카드충전액(천원) |
141 |
0.83 |
27.56 |
24.82 |
0.02 |
9.11 |
19.30 |
38.87 |
158.70 |
▇▃▁▁▁ |
| 월별카드사용액(천원) |
141 |
0.83 |
24.94 |
22.76 |
0.00 |
7.40 |
17.18 |
34.67 |
101.70 |
▇▃▂▁▁ |
| total |
0 |
1.00 |
1.00 |
0.00 |
1.00 |
1.00 |
1.00 |
1.00 |
1.00 |
▁▁▇▁▁ |
RCBD
rcbd_card = aov(`월별카드발행수량(건)`~시군명+covid+기준년월, data=data)
summary(rcbd_card)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 27 2.603e+10 9.639e+08 4.594 6.78e-13 ***
## covid 1 1.624e+10 1.624e+10 77.405 < 2e-16 ***
## 기준년월 23 6.936e+10 3.016e+09 14.371 < 2e-16 ***
## Residuals 626 1.314e+11 2.098e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 159 observations deleted due to missingness
rcbd_card_use = aov(`월별카드사용액(천원)`~시군명+covid+기준년월, data=data)
summary(rcbd_card_use)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 28 9.064e+15 3.237e+14 33.164 < 2e-16 ***
## covid 1 5.738e+15 5.738e+15 587.844 < 2e-16 ***
## 기준년월 25 8.558e+14 3.423e+13 3.507 3.05e-08 ***
## Residuals 641 6.257e+15 9.761e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness
rcbd_card_charge = aov(`월별카드충전액(천원)`~시군명+covid+기준년월, data=data)
summary(rcbd_card_charge)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 28 1.097e+16 3.918e+14 25.935 <2e-16 ***
## covid 1 6.400e+15 6.400e+15 423.693 <2e-16 ***
## 기준년월 25 5.988e+14 2.395e+13 1.586 0.0356 *
## Residuals 641 9.683e+15 1.511e+13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness
월별카드발행수량(건)
residuals = resid(rcbd_card)
fitted = fitted(rcbd_card)
plot(fitted, residuals, pch=20, ylim=c(-1.1,1.1)*max(abs(residuals)),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(residuals, datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(residuals, datax = T)

월별카드사용액(천원)
residuals_use = resid(rcbd_card_use)
fitted_use = fitted(rcbd_card_use)
plot(fitted_use, residuals_use, pch=20, ylim=c(-1.1,1.1)*max(abs(residuals_use)),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(residuals_use, datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(residuals_use, datax = T)

월별카드충전액(천원)
residuals_charge = resid(rcbd_card_charge)
fitted_charge = fitted(rcbd_card_charge)
plot(fitted_charge , residuals_charge , pch=20, ylim=c(-1.1,1.1)*max(abs(residuals_charge )),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(residuals_charge , datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(residuals_charge , datax = T)

div data
div_rcbd_card = aov(`월별카드발행수량(건)`~시군명+covid+기준년월, data=div_data)
summary(div_rcbd_card)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 27 7.840e-27 2.902e-28 0.965 0.517
## covid 1 3.200e-28 3.247e-28 1.080 0.299
## 기준년월 23 6.670e-27 2.901e-28 0.965 0.510
## Residuals 626 1.882e-25 3.007e-28
## 159 observations deleted due to missingness
div_rcbd_card_use = aov(`월별카드사용액(천원)`~시군명+covid+기준년월, data=div_data)
summary(div_rcbd_card_use)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 27 8.988e+15 3.329e+14 35.117 < 2e-16 ***
## covid 1 5.800e+15 5.800e+15 611.856 < 2e-16 ***
## 기준년월 23 9.930e+14 4.317e+13 4.554 2.68e-11 ***
## Residuals 617 5.849e+15 9.479e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 168 observations deleted due to missingness
div_rcbd_card_charge = aov(`월별카드충전액(천원)`~시군명+covid+기준년월, data=div_data)
summary(div_rcbd_card_charge)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 27 1.082e+16 4.006e+14 29.784 < 2e-16 ***
## covid 1 6.382e+15 6.382e+15 474.509 < 2e-16 ***
## 기준년월 23 7.139e+14 3.104e+13 2.308 0.000535 ***
## Residuals 617 8.298e+15 1.345e+13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 168 observations deleted due to missingness
월별카드사용액(천원)
div_residuals_use = resid(div_rcbd_card_use)
div_fitted_use = fitted(div_rcbd_card_use)
plot(div_fitted_use, div_residuals_use, pch=20, ylim=c(-1.1,1.1)*max(abs(div_residuals_use)),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(div_residuals_use, datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(residuals_use, datax = T)

월별카드충전액(천원)
div_residuals_charge = resid(div_rcbd_card_charge)
div_fitted_charge = fitted(div_rcbd_card_charge)
plot(div_fitted_charge , div_residuals_charge , pch=20, ylim=c(-1.1,1.1)*max(abs(div_residuals_charge )),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(div_residuals_charge , datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(div_residuals_charge , datax = T)

RCBD with standard?
leveneTest(`월별카드발행수량(건)`~시군명, data=data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 27 1.7554 0.0109 *
## 650
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(`월별카드사용액(천원)`~시군명, data=data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 28 28.829 < 2.2e-16 ***
## 667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(`월별카드충전액(천원)`~시군명, data=data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 28 19.641 < 2.2e-16 ***
## 667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(`월별카드발행수량(건)`~시군명, data=div_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 27
## 650
leveneTest(`월별카드사용액(천원)`~시군명, data=div_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 27 29.511 < 2.2e-16 ***
## 641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(`월별카드충전액(천원)`~시군명, data=div_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 27 24.092 < 2.2e-16 ***
## 641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(`월별카드발행수량(건)`~시군명, data=stand_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 27 0.4645 0.9912
## 650
leveneTest(`월별카드사용액(천원)`~시군명, data=stand_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 28 8.7485 < 2.2e-16 ***
## 667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
leveneTest(`월별카드충전액(천원)`~시군명, data=stand_data)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 28 8.6391 < 2.2e-16 ***
## 667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
stand data
stand_rcbd_card = aov(`월별카드발행수량(건)`~시군명*covid+기준년월, data=data)
summary(stand_rcbd_card)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 27 2.603e+10 9.639e+08 4.896 5.11e-14 ***
## covid 1 1.624e+10 1.624e+10 82.498 < 2e-16 ***
## 기준년월 23 6.936e+10 3.016e+09 15.317 < 2e-16 ***
## 시군명:covid 27 1.342e+10 4.972e+08 2.525 4.22e-05 ***
## Residuals 599 1.179e+11 1.969e+08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 159 observations deleted due to missingness
stand_rcbd_card_use = aov(`월별카드사용액(천원)`~시군명*covid+기준년월, data=data)
summary(stand_rcbd_card_use)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 28 9.064e+15 3.237e+14 158.24 <2e-16 ***
## covid 1 5.738e+15 5.738e+15 2804.75 <2e-16 ***
## 기준년월 25 8.558e+14 3.423e+13 16.73 <2e-16 ***
## 시군명:covid 28 5.002e+15 1.787e+14 87.33 <2e-16 ***
## Residuals 613 1.254e+15 2.046e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness
stand_rcbd_card_charge = aov(`월별카드충전액(천원)`~시군명*covid+기준년월, data=data)
summary(stand_rcbd_card_charge)
## Df Sum Sq Mean Sq F value Pr(>F)
## 시군명 28 1.097e+16 3.918e+14 55.595 < 2e-16 ***
## covid 1 6.400e+15 6.400e+15 908.224 < 2e-16 ***
## 기준년월 25 5.988e+14 2.395e+13 3.399 7.81e-08 ***
## 시군명:covid 28 5.363e+15 1.915e+14 27.180 < 2e-16 ***
## Residuals 613 4.320e+15 7.047e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 141 observations deleted due to missingness
월별카드발행수량(건)
stand_residuals = resid(stand_rcbd_card)
stand_fitted = fitted(stand_rcbd_card)
plot(stand_fitted, stand_residuals, pch=20, ylim=c(-1.1,1.1)*max(abs(stand_residuals)),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(stand_residuals, datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(stand_residuals, datax = T)

월별카드사용액(천원)
stand_residuals_use = resid(stand_rcbd_card_use)
stand_fitted_use = fitted(stand_rcbd_card_use)
plot(stand_fitted_use, stand_residuals_use, pch=20, ylim=c(-1.1,1.1)*max(abs(stand_residuals_use)),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(stand_residuals_use, datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(stand_residuals_use, datax = T)

월별카드충전액(천원)
stand_residuals_charge = resid(stand_rcbd_card_charge)
stand_fitted_charge = fitted(stand_rcbd_card_charge)
plot(stand_fitted_charge , stand_residuals_charge , pch=20, ylim=c(-1.1,1.1)*max(abs(stand_residuals_charge)),
xlab = 'fitted value', ylab='residual', main='residuals vs fitted values')
abline(0,0,lty=2)

qqnorm(stand_residuals_charge , datax=T, ylab='normal scores',
xlab='residual', main='normal probability plot of the residuals')
qqline(stand_residuals_charge , datax = T)

Tukey-Kramer Procedure
coef = stand_rcbd_card$coefficients%>% as.data.frame()
rownames(coef)
## [1] "(Intercept)" "시군명고양시"
## [3] "시군명과천시" "시군명광명시"
## [5] "시군명광주시" "시군명구리시"
## [7] "시군명군포시" "시군명남양주시"
## [9] "시군명동두천시" "시군명부천시"
## [11] "시군명수원시" "시군명안산시"
## [13] "시군명안성시" "시군명안양시"
## [15] "시군명양주시" "시군명양평군"
## [17] "시군명여주시" "시군명연천군"
## [19] "시군명오산시" "시군명용인시"
## [21] "시군명의왕시" "시군명의정부시"
## [23] "시군명이천시" "시군명파주시"
## [25] "시군명평택시" "시군명포천시"
## [27] "시군명하남시" "시군명화성시"
## [29] "covidbefore" "기준년월2019-04"
## [31] "기준년월2019-05" "기준년월2019-06"
## [33] "기준년월2019-07" "기준년월2019-08"
## [35] "기준년월2019-09" "기준년월2019-10"
## [37] "기준년월2019-11" "기준년월2019-12"
## [39] "기준년월2020-01" "기준년월2020-02"
## [41] "기준년월2020-03" "기준년월2020-04"
## [43] "기준년월2020-05" "기준년월2020-06"
## [45] "기준년월2020-07" "기준년월2020-08"
## [47] "기준년월2020-09" "기준년월2020-10"
## [49] "기준년월2020-11" "기준년월2020-12"
## [51] "기준년월2021-01" "기준년월2021-02"
## [53] "기준년월2021-03" "시군명고양시:covidbefore"
## [55] "시군명과천시:covidbefore" "시군명광명시:covidbefore"
## [57] "시군명광주시:covidbefore" "시군명구리시:covidbefore"
## [59] "시군명군포시:covidbefore" "시군명남양주시:covidbefore"
## [61] "시군명동두천시:covidbefore" "시군명부천시:covidbefore"
## [63] "시군명수원시:covidbefore" "시군명안산시:covidbefore"
## [65] "시군명안성시:covidbefore" "시군명안양시:covidbefore"
## [67] "시군명양주시:covidbefore" "시군명양평군:covidbefore"
## [69] "시군명여주시:covidbefore" "시군명연천군:covidbefore"
## [71] "시군명오산시:covidbefore" "시군명용인시:covidbefore"
## [73] "시군명의왕시:covidbefore" "시군명의정부시:covidbefore"
## [75] "시군명이천시:covidbefore" "시군명파주시:covidbefore"
## [77] "시군명평택시:covidbefore" "시군명포천시:covidbefore"
## [79] "시군명하남시:covidbefore" "시군명화성시:covidbefore"
a = tibble(name=rownames(coef)[str_detect(rownames(coef), ':covid')],
coef=coef[str_detect(rownames(coef), ':covid'),]) %>%
arrange(coef)
coef = stand_rcbd_card_use$coefficients %>% as.data.frame()
b = tibble(name=rownames(coef)[str_detect(rownames(coef), ':covid')],
coef=coef[str_detect(rownames(coef), ':covid'),]) %>%
arrange(coef)
coef = stand_rcbd_card_charge$coefficients %>% as.data.frame()
c = tibble(name=rownames(coef)[str_detect(rownames(coef), ':covid')],
coef=coef[str_detect(rownames(coef), ':covid'),]) %>%
arrange(coef)
tukey_stand_rcbd_card = TukeyHSD(x=stand_rcbd_card, '시군명', conf.level=0.95)
tukey_stand_rcbd_card_use = TukeyHSD(x=stand_rcbd_card_use, '시군명', conf.level=0.95)
tukey_stand_rcbd_card_charge = TukeyHSD(x=stand_rcbd_card_charge, '시군명', conf.level=0.95)
plot_tukey = function(tukey, str){
rowname_vector = rownames(tukey$시군명)
tukey$시군명 = tukey$시군명[str_detect(rowname_vector, str),]
print(tukey$시군명)
plot(tukey, las=1, tcl=-.3, col="brown")
}
par(family = 'NanumGothic')
par(mar=c(3,7,3,3))
plot_tukey(tukey_stand_rcbd_card, '수원')
## diff lwr upr p adj
## 수원시-가평군 20710.0800 5895.7597 35524.40027 8.923641e-05
## 수원시-고양시 5113.7433 -9854.0973 20081.58397 9.999796e-01
## 수원시-과천시 20207.4517 5239.6110 35175.29230 2.178021e-04
## 수원시-광명시 16814.4800 2000.1597 31628.80027 7.870744e-03
## 수원시-광주시 14746.4933 -221.3473 29714.33397 5.988209e-02
## 수원시-구리시 16860.6817 1727.7375 31993.62599 1.070741e-02
## 수원시-군포시 15929.7850 961.9444 30897.62564 2.166405e-02
## 수원시-남양주시 11738.6600 -3229.1806 26706.50064 4.120775e-01
## 수원시-동두천시 19717.1600 4749.3194 34685.00064 3.935222e-04
## 수원시-부천시 8396.7200 -6417.6003 23211.04027 9.433380e-01
## 안산시-수원시 -3049.6000 -17863.9203 11764.72027 1.000000e+00
## 안성시-수원시 -18316.3600 -33130.6803 -3502.03973 1.586231e-03
## 안양시-수원시 -13239.0767 -28206.9173 1728.76397 1.791266e-01
## 양주시-수원시 -18215.4000 -33029.7203 -3401.07973 1.775797e-03
## 양평군-수원시 -19439.1200 -34253.4403 -4624.79973 4.310255e-04
## 여주시-수원시 -19703.0400 -34517.3603 -4888.71973 3.134467e-04
## 연천군-수원시 -20674.6817 -35807.6260 -5541.73749 1.618170e-04
## 오산시-수원시 -14714.5200 -29528.8403 99.80027 5.431310e-02
## 용인시-수원시 -3661.9517 -18629.7923 11305.88897 1.000000e+00
## 의왕시-수원시 -18894.1600 -34027.1043 -3761.21575 1.294761e-03
## 의정부시-수원시 -14843.5350 -29811.3756 124.30564 5.536159e-02
## 이천시-수원시 -18320.8683 -33288.7090 -3353.02770 1.946229e-03
## 파주시-수원시 -14836.0767 -29803.9173 131.76397 5.569837e-02
## 평택시-수원시 -16180.9426 -31313.8869 -1047.99836 2.023376e-02
## 포천시-수원시 -17958.2017 -32926.0423 -2990.36103 2.884008e-03
## 하남시-수원시 -16030.4933 -30998.3340 -1062.65270 1.975410e-02
## 화성시-수원시 -557.4517 -15525.2923 14410.38897 1.000000e+00

plot_tukey(tukey_stand_rcbd_card, '연천')
## diff lwr upr p adj
## 연천군-가평군 35.39826 -15097.546 15168.343 1.0000000000
## 연천군-고양시 -15560.93841 -30844.203 -277.674 0.0398092448
## 연천군-과천시 -467.23007 -15750.494 14816.034 1.0000000000
## 연천군-광명시 -3860.20174 -18993.146 11272.743 0.9999999603
## 연천군-광주시 -5928.18841 -21211.453 9355.076 0.9997763015
## 연천군-구리시 -3814.00000 -19258.997 11630.997 0.9999999811
## 연천군-군포시 -4744.89674 -20028.161 10538.368 0.9999970897
## 연천군-남양주시 -8936.02174 -24219.286 6347.243 0.9216108919
## 연천군-동두천시 -957.52174 -16240.786 14325.743 1.0000000000
## 연천군-부천시 -12277.96174 -27410.906 2854.983 0.3382082534
## 연천군-수원시 -20674.68174 -35807.626 -5541.737 0.0001618170
## 연천군-안산시 -17625.08174 -32758.026 -2492.137 0.0050130528
## 연천군-안성시 -2358.32174 -17491.266 12774.623 1.0000000000
## 연천군-안양시 -7435.60507 -22718.869 7847.659 0.9914892342
## 연천군-양주시 -2459.28174 -17592.226 12673.663 1.0000000000
## 연천군-양평군 -1235.56174 -16368.506 13897.383 1.0000000000
## 연천군-여주시 -971.64174 -16104.586 14161.303 1.0000000000
## 오산시-연천군 5960.16174 -9172.783 21093.106 0.9997062536
## 용인시-연천군 17012.73007 1729.466 32295.994 0.0108661281
## 의왕시-연천군 1780.52174 -13664.475 17225.518 1.0000000000
## 의정부시-연천군 5831.14674 -9452.118 21114.411 0.9998340450
## 이천시-연천군 2353.81341 -12929.451 17637.078 1.0000000000
## 파주시-연천군 5838.60507 -9444.659 21121.869 0.9998301379
## 평택시-연천군 4493.73913 -10951.257 19938.736 0.9999992662
## 포천시-연천군 2716.48007 -12566.784 17999.744 1.0000000000
## 하남시-연천군 4644.18841 -10639.076 19927.453 0.9999981452
## 화성시-연천군 20117.23007 4833.966 35400.494 0.0004006694

plot_tukey(tukey_stand_rcbd_card_use, '수원')
## diff lwr upr p adj
## 수원시-가평군 9482238.37 7933937.6 11030539.2 3.317751e-10
## 수원시-고양시 70317.17 -1477983.6 1618618.0 1.000000e+00
## 수원시-과천시 8810220.25 7261919.5 10358521.0 3.317751e-10
## 수원시-광명시 6778280.12 5229979.3 8326580.9 3.317751e-10
## 수원시-광주시 5430159.92 3881859.1 6978460.7 3.317751e-10
## 수원시-구리시 7695368.97 6130329.3 9260408.6 3.317751e-10
## 수원시-군포시 5132314.75 3584014.0 6680615.5 3.317751e-10
## 수원시-남양주시 4280722.25 2732421.5 5829023.0 3.320545e-10
## 수원시-동두천시 9323416.79 7775116.0 10871717.6 3.317751e-10
## 수원시-부천시 1032210.71 -516090.1 2580511.5 7.644359e-01
## 수원시-성남시 3969171.83 2464493.9 5473849.7 3.320553e-10
## 안산시-수원시 404696.08 -1143604.7 1952996.9 1.000000e+00
## 안성시-수원시 -7330487.46 -8878788.3 -5782186.7 3.317751e-10
## 안양시-수원시 -6788961.62 -8337262.4 -5240660.8 3.317751e-10
## 양주시-수원시 -8045302.19 -9578041.8 -6512562.6 3.317751e-10
## 양평군-수원시 -7490324.46 -9038625.3 -5942023.7 3.317751e-10
## 여주시-수원시 -8575017.00 -10123317.8 -7026716.2 3.317751e-10
## 연천군-수원시 -9491732.40 -11056772.1 -7926692.7 3.317751e-10
## 오산시-수원시 -6580762.62 -8129063.4 -5032461.8 3.317751e-10
## 용인시-수원시 -1681266.21 -3229567.0 -132965.4 1.589822e-02
## 의왕시-수원시 -8973580.36 -10538620.0 -7408540.7 3.317751e-10
## 의정부시-수원시 -6897708.37 -8446009.2 -5349407.6 3.317751e-10
## 이천시-수원시 -7981105.12 -9529405.9 -6432804.3 3.317751e-10
## 파주시-수원시 -6474328.92 -8022629.7 -4926028.1 3.317751e-10
## 평택시-수원시 -7428701.40 -8993741.1 -5863661.7 3.317751e-10
## 포천시-수원시 -9254149.79 -10802450.6 -7705849.0 3.317751e-10
## 하남시-수원시 -4040798.29 -5589099.1 -2492497.5 3.320502e-10
## 화성시-수원시 5220244.54 3671943.7 6768545.3 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_use, '연천')
## diff lwr upr p adj
## 연천군-가평군 -9494.025 -1574533.69 1555545.6 1.000000e+00
## 연천군-고양시 -9421415.234 -10986454.90 -7856375.6 3.317751e-10
## 연천군-과천시 -681512.150 -2246551.82 883527.5 9.986718e-01
## 연천군-광명시 -2713452.275 -4278491.94 -1148412.6 6.687843e-08
## 연천군-광주시 -4061572.484 -5626612.15 -2496532.8 3.320377e-10
## 연천군-구리시 -1796363.435 -3377964.83 -214762.0 7.652191e-03
## 연천군-군포시 -4359417.650 -5924457.32 -2794378.0 3.320531e-10
## 연천군-남양주시 -5211010.150 -6776049.82 -3645970.5 3.317751e-10
## 연천군-동두천시 -168315.609 -1733355.28 1396724.1 1.000000e+00
## 연천군-부천시 -8459521.692 -10024561.36 -6894482.0 3.317751e-10
## 연천군-성남시 -5522560.572 -7044457.22 -4000663.9 3.317751e-10
## 연천군-수원시 -9491732.400 -11056772.07 -7926692.7 3.317751e-10
## 연천군-안산시 -9896428.484 -11461468.15 -8331388.8 3.317751e-10
## 연천군-안성시 -2161244.942 -3726284.61 -596205.3 1.162333e-04
## 연천군-안양시 -2702770.775 -4267810.44 -1137731.1 7.831811e-08
## 연천군-양주시 -1446430.209 -2996076.77 103216.4 1.090932e-01
## 연천군-양평군 -2001407.942 -3566447.61 -436368.3 7.438081e-04
## 연천군-여주시 -916715.400 -2481755.07 648324.3 9.251763e-01
## 오산시-연천군 2910969.775 1345930.11 4476009.4 3.553725e-09
## 용인시-연천군 7810466.192 6245426.52 9375505.9 3.317751e-10
## 의왕시-연천군 518152.043 -1063449.35 2099753.4 9.999939e-01
## 의정부시-연천군 2594024.025 1028984.36 4159063.7 3.806146e-07
## 이천시-연천군 1510627.275 -54412.39 3075666.9 7.608621e-02
## 파주시-연천군 3017403.484 1452363.82 4582443.2 9.178752e-10
## 평택시-연천군 2063031.000 481429.60 3644632.4 4.740042e-04
## 포천시-연천군 237582.609 -1327457.06 1802622.3 1.000000e+00
## 하남시-연천군 5450934.109 3885894.44 7015973.8 3.317751e-10
## 화성시-연천군 14711976.942 13146937.27 16277016.6 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_use, '성남')
## diff lwr upr p adj
## 성남시-가평군 5513066.55 4008388.64 7017744 3.317751e-10
## 성남시-고양시 -3898854.66 -5403532.57 -2394177 3.320397e-10
## 성남시-과천시 4841048.42 3336370.51 6345726 3.317764e-10
## 성남시-광명시 2809108.30 1304430.39 4313786 3.047002e-09
## 성남시-광주시 1460988.09 -43689.82 2965666 7.113015e-02
## 성남시-구리시 3726197.14 2204300.48 5248094 3.320424e-10
## 성남시-군포시 1163142.92 -341534.99 2667821 4.503250e-01
## 성남시-남양주시 311550.42 -1193127.49 1816228 1.000000e+00
## 성남시-동두천시 5354244.96 3849567.05 6858923 3.317751e-10
## 성남시-부천시 -2936961.12 -4441639.03 -1432283 6.501800e-10
## 수원시-성남시 3969171.83 2464493.92 5473850 3.320553e-10
## 안산시-성남시 4373867.91 2869190.00 5878546 3.319769e-10
## 안성시-성남시 -3361315.63 -4865993.54 -1856638 3.322228e-10
## 안양시-성남시 -2819789.80 -4324467.71 -1315112 2.608595e-09
## 양주시-성남시 -4076130.36 -5564791.14 -2587470 3.320528e-10
## 양평군-성남시 -3521152.63 -5025830.54 -2016475 3.320260e-10
## 여주시-성남시 -4605845.17 -6110523.08 -3101167 3.318189e-10
## 연천군-성남시 -5522560.57 -7044457.22 -4000664 3.317751e-10
## 오산시-성남시 -2611590.80 -4116268.71 -1106913 6.406239e-08
## 용인시-성남시 2287905.62 783227.71 3792584 7.299669e-06
## 의왕시-성남시 -5004408.53 -6526305.18 -3482512 3.317752e-10
## 의정부시-성남시 -2928536.55 -4433214.46 -1423859 6.993404e-10
## 이천시-성남시 -4011933.30 -5516611.21 -2507255 3.320610e-10
## 파주시-성남시 -2505157.09 -4009835.00 -1000479 3.222046e-07
## 평택시-성남시 -3459529.57 -4981426.22 -1937633 3.321003e-10
## 포천시-성남시 -5284977.96 -6789655.87 -3780300 3.317751e-10
## 하남시-성남시 -71626.46 -1576304.37 1433051 1.000000e+00
## 화성시-성남시 9189416.37 7684738.46 10694094 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_charge, '수원')
## diff lwr upr p adj
## 수원시-가평군 10957886.5 8084179 13831594.4 3.317751e-10
## 수원시-고양시 477929.6 -2395778 3351637.5 1.000000e+00
## 수원시-과천시 10193149.0 7319441 13066856.9 3.317751e-10
## 수원시-광명시 7895593.6 5021886 10769301.5 3.320437e-10
## 수원시-광주시 6581617.8 3707910 9455325.7 3.320707e-10
## 수원시-구리시 8978232.9 6073457 11883008.9 3.317994e-10
## 수원시-군포시 6226485.2 3352777 9100193.2 3.331129e-10
## 수원시-남양주시 5193745.5 2320038 8067453.4 1.197980e-08
## 수원시-동두천시 10790583.9 7916876 13664291.8 3.317751e-10
## 수원시-부천시 1709122.1 -1164586 4582830.0 9.126072e-01
## 수원시-성남시 4288564.8 1495823 7081307.0 5.331953e-06
## 안산시-수원시 -132158.5 -3005866 2741549.5 1.000000e+00
## 안성시-수원시 -8583520.7 -11457229 -5709812.8 3.318819e-10
## 안양시-수원시 -7829529.0 -10703237 -4955821.0 3.320597e-10
## 양주시-수원시 -9365922.2 -12210748 -6521096.5 3.317752e-10
## 양평군-수원시 -8848627.7 -11722336 -5974919.8 3.318041e-10
## 여주시-수원시 -9988836.4 -12862544 -7115128.5 3.317751e-10
## 연천군-수원시 -10969115.3 -13873891 -8064339.4 3.317751e-10
## 오산시-수원시 -7744098.6 -10617807 -4870390.7 3.320535e-10
## 용인시-수원시 -2019715.5 -4893423 853992.4 6.632813e-01
## 의왕시-수원시 -10321026.6 -13225803 -7416250.6 3.317751e-10
## 의정부시-수원시 -7983010.9 -10856719 -5109303.0 3.320444e-10
## 이천시-수원시 -9260602.6 -12134311 -6386894.7 3.317763e-10
## 파주시-수원시 -7632338.2 -10506046 -4758630.3 3.320338e-10
## 평택시-수원시 -8550307.1 -11455083 -5645531.2 3.319314e-10
## 포천시-수원시 -10665737.9 -13539446 -7792030.0 3.317751e-10
## 하남시-수원시 -4697028.9 -7570737 -1823321.0 6.348474e-07
## 화성시-수원시 4712960.6 1839253 7586668.5 5.615003e-07

plot_tukey(tukey_stand_rcbd_card_charge, '연천')
## diff lwr upr p adj
## 연천군-가평군 -11228.85 -2916004.79 2893547.1 1.000000e+00
## 연천군-고양시 -10491185.77 -13395961.70 -7586409.8 3.317751e-10
## 연천군-과천시 -775966.35 -3680742.29 2128809.6 9.999999e-01
## 연천군-광명시 -3073521.72 -5978297.66 -168745.8 2.340800e-02
## 연천군-광주시 -4387497.56 -7292273.49 -1482721.6 8.991586e-06
## 연천군-구리시 -1990882.43 -4926397.60 944632.7 7.336172e-01
## 연천군-군포시 -4742630.10 -7647406.04 -1837854.2 6.603644e-07
## 연천군-남양주시 -5775369.85 -8680145.79 -2870593.9 4.561849e-10
## 연천군-동두천시 -178531.43 -3083307.37 2726244.5 1.000000e+00
## 연천군-부천시 -9259993.22 -12164769.16 -6355217.3 3.317771e-10
## 연천군-성남시 -6680550.50 -9505251.28 -3855849.7 3.320264e-10
## 연천군-수원시 -10969115.35 -13873891.29 -8064339.4 3.317751e-10
## 연천군-안산시 -10836956.89 -13741732.83 -7932181.0 3.317751e-10
## 연천군-안성시 -2385594.64 -5290370.58 519181.3 3.168145e-01
## 연천군-안양시 -3139586.39 -6044362.33 -234810.5 1.707327e-02
## 연천군-양주시 -1603193.11 -4479398.82 1273012.6 9.564891e-01
## 연천군-양평군 -2120487.60 -5025263.54 784288.3 5.811217e-01
## 연천군-여주시 -980278.93 -3885054.87 1924497.0 9.999887e-01
## 오산시-연천군 3225016.77 320240.83 6129792.7 1.118846e-02
## 용인시-연천군 8949399.85 6044623.91 11854175.8 3.318038e-10
## 의왕시-연천군 648088.78 -2287426.38 3583603.9 1.000000e+00
## 의정부시-연천군 2986104.47 81328.54 5890880.4 3.498497e-02
## 이천시-연천군 1708512.72 -1196263.21 4613288.7 9.219154e-01
## 파주시-연천군 3336777.14 432001.20 6241553.1 6.285249e-03
## 평택시-연천군 2418808.22 -516706.95 5354323.4 3.099849e-01
## 포천시-연천군 303377.43 -2601398.50 3208153.4 1.000000e+00
## 하남시-연천군 6272086.47 3367310.54 9176862.4 3.333511e-10
## 화성시-연천군 15682075.93 12777300.00 18586851.9 3.317751e-10

plot_tukey(tukey_stand_rcbd_card_charge, '성남')
## diff lwr upr p adj
## 성남시-가평군 6669321.7 3876579.5 9462063.8 3.320342e-10
## 성남시-고양시 -3810635.3 -6603377.4 -1017893.2 1.583905e-04
## 성남시-과천시 5904584.2 3111842.0 8697326.3 3.363515e-10
## 성남시-광명시 3607028.8 814286.7 6399770.9 5.948710e-04
## 성남시-광주시 2293052.9 -499689.2 5085795.1 3.172969e-01
## 성남시-구리시 4689668.1 1864967.3 7514368.8 3.578512e-07
## 성남시-군포시 1937920.4 -854821.7 4730662.5 6.893489e-01
## 성남시-남양주시 905180.7 -1887561.5 3697922.8 9.999951e-01
## 성남시-동두천시 6502019.1 3709277.0 9294761.2 3.320358e-10
## 성남시-부천시 -2579442.7 -5372184.8 213299.4 1.212611e-01
## 수원시-성남시 4288564.8 1495822.7 7081307.0 5.331953e-06
## 안산시-성남시 4156406.4 1363664.3 6949148.5 1.414926e-05
## 안성시-성남시 -4294955.9 -7087698.0 -1502213.8 5.082532e-06
## 안양시-성남시 -3540964.1 -6333706.2 -748222.0 8.987126e-04
## 양주시-성남시 -5077357.4 -7840371.1 -2314343.7 5.915691e-09
## 양평군-성남시 -4560062.9 -7352805.0 -1767320.8 6.585358e-07
## 여주시-성남시 -5700271.6 -8493013.7 -2907529.5 3.628389e-10
## 연천군-성남시 -6680550.5 -9505251.3 -3855849.7 3.320264e-10
## 오산시-성남시 -3455533.7 -6248275.8 -662791.6 1.513036e-03
## 용인시-성남시 2268849.3 -523892.8 5061591.5 3.396641e-01
## 의왕시-성남시 -6032461.7 -8857162.5 -3207760.9 3.344764e-10
## 의정부시-성남시 -3694446.0 -6487188.1 -901703.9 3.402205e-04
## 이천시-성남시 -4972037.8 -7764779.9 -2179295.7 2.257006e-08
## 파주시-성남시 -3343773.4 -6136515.5 -551031.3 2.925045e-03
## 평택시-성남시 -4261742.3 -7086443.1 -1437041.5 9.312256e-06
## 포천시-성남시 -6377173.1 -9169915.2 -3584431.0 3.320813e-10
## 하남시-성남시 -408464.0 -3201206.1 2384278.1 1.000000e+00
## 화성시-성남시 9001525.4 6208783.3 11794267.5 3.317763e-10
